Abstract
This article addresses the issue of selecting Financial Strategies in Multi-National companies (F.S.M.). The F.S.M. typically has to consider multiple factors involving multiple stakeholders and, hence, can be handled by applying an appropriate Multi-Criteria Group Decision-Making (M.C.G.D.M.) approach. To address this issue, we develop an M.C.G.D.M. framework to tackle the F.S.M. problem. To handle inherent uncertainty in business decisions as reflected by linguistic reasoning, we embark on constructing a Linguistic Pythagorean Fuzzy (L.P.F.) M.C.G.D.M. framework that is capable of tackling both uncertain decision information and linguistic variables. The proposed approach extends the combinative distance-based assessment (C.O.D.A.S.) method into the L.P.F. environment, and processes decision input expressed as Pythagorean fuzzy sets (P.F.S.) and pure linguistic variables (rather than converting linguistic information into fuzzy numbers). The developed L.P.F.-C.O.D.A.S. technique aggregates the L.P.F. information and is applied to the F.S.M. problem with uncertain linguistic information. A comparative analysis is carried out to compare the results obtained from the proposed L.P.F.-C.O.D.A.S. approach with those from other extensions of C.O.D.A.S. Furthermore, a sensitivity analysis is conducted to check the impact of changes in a distance threshold parameter on the ranking results.
Highlights
Multi-criteria group decision-making (M.C.G.D.M.) methods are important in solving managerial problems in many areas, ranging from location selection, to supply chain management and financial management (Dorokhov et al, 2018; Liu, He, & Xu, 2019; Pongpimol et al, 2020)
The linguistic information is maintained throughout the aggregation process
A comparative analysis and sensitivity analysis are carried out to check the performance of the proposed method
Summary
Multi-criteria group decision-making (M.C.G.D.M.) methods are important in solving managerial problems in many areas, ranging from location selection, to supply chain management and financial management (Dorokhov et al, 2018; Liu, He, & Xu, 2019; Pongpimol et al, 2020). Due to increasing complexity of the real world decision problems, different M.C.D.G.M. methods have been proposed to cope with uncertainty characterised by the fuzzy information (Chen, 2000; Wang & Chang, 2005; Keshavarz Ghorabaee et al, 2016; Gao, Lu, & Wei, 2019). They have been employed to identify the most desirable solutions in many different applications (Aliakbari Nouri, Khalili Esbouei, & Antucheviciene, 2015; Vinodh, Sai Balagi, & Patil, 2016; Hosseini et al, 2016). The fuzzy set theory (Zadeh, 1965) relaxed those assumptions by allowing for partial membership degrees between 0 and 1 In this instance, M.C.G.D.M. models can handle vague information.
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